Minimally invasive surgical techniques can reduce the risk of injury to patients, in comparison with traditional surgical techniques. However, accurate surface rendering (e.g. skin, cortical surfaces and the like) at an image rendering device can be critical when planning a surgery and/or performing a skin or brain surface based registration during a navigated procedure. Such surfaces may be determined based on the contents of a volumetric magnetic resonance (MR) imaging scan or computerized tomography scan using an algorithm that determines portions of imaging data that represent patient tissue and portions that represent regions adjacent patient tissue (e.g. air/hair/padding etc.). While such algorithms may do a reasonable job of extracting a surface, errors in the imaging data may cause the algorithms to inaccurately extract a surface. For example, in MR imaging, signal inhomogeneity may lead to a “cratered” skin surface appearance in the rendering of the imaging data. While this can be addressed by forcing the extraction algorithm to use a lower threshold value to extract a surface, the imaging datasets are generally very large and models used to extract surfaces must be carefully refined surgical purposes, causing the image rendering device to operate inefficiently when the extraction is performed repeatedly. For example, even a single adjustment to a threshold value may require the imaging device to use tens of seconds to re-determine a surface, and the imaging device will not be able to provide any indication of whether the adjustment was successful until the algorithm has completed. This causes the image rendering device to operate inefficiently, especially when several adjustments to the threshold value are made to achieve an acceptable result.